Describing the Diet of Juvenile White Sturgeon in the Upper Columbia River Canada with Lethal and Nonlethal Methods
Bibliographic record
Abstract
Abstract We describe the overall composition and prey selectivity in the diet of hatchery-reared juvenile White Sturgeon Acipenser transmontanus in the upper Columbia River, Canada. The efficacy of two sampling methods, nonlethal gastric lavage and lethal sampling to remove stomach contents, were evaluated across different ages, size-classes, and river sections. Gastric lavage samples were collected from 108 fish angled in October 2012 and 2013. In 2012 only, a subsample of 48 individuals were euthanized following gastric lavage, and stomach contents were collected. To describe food availability, 45 benthic grabs were collected from areas of juvenile capture. Identifiable prey taxa were recovered from 60.3% of lavage and 98% of lethal stomachs sampled. While the diet of juvenile White Sturgeon was composed of 16 diverse prey taxa, most were selected less than their availability in the river. Prey diversity in lethal samples was influenced by river section, not by fish size or age; fish in deeper, slower water consumed the highest number of prey taxa (mean = 4.6). Further, there was no significant overlap in diets among river sections, the dominant prey taxa selected differing among river sections and years. Prey in the lethal samples included 56% of the 25 total prey taxa identified, lethal included 60%, and the bottom grabs included 76%. Gastric lavage was 69% efficient at describing lethal samples. A minimum of 100 lavage samples were required to describe the diet to a level comparable to lethal sampling. Where lethal sampling is not an option, our results indicate gastric lavage, if conducted on appropriate numbers of fish, is effective at describing sturgeon diets and provides data that can be used to study the feeding ecology of threatened or endangered species. Received August 24, 2015; accepted November 24, 2015 Published online March 31, 2016
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".